no code implementations • 21 May 2023 • Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan
To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.
no code implementations • 11 May 2023 • Ming Jin, Guangsi Shi, Yuan-Fang Li, Qingsong Wen, Bo Xiong, Tian Zhou, Shirui Pan
In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.
1 code implementation • 28 Apr 2023 • Hoang Anh Just, Feiyang Kang, Jiachen T. Wang, Yi Zeng, Myeongseob Ko, Ming Jin, Ruoxi Jia
(1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between the training and the validation set.
no code implementations • 24 Apr 2023 • Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui Pan, Steffen Staab
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.
Hierarchical Multi-label Classification
Knowledge Graph Completion
+1
no code implementations • 4 Dec 2022 • Vanshaj Khattar, Ming Jin
Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high power demand peaks, grid instability exacerbated by intermittent renewable generation, and global climate change amplified by rising carbon emissions.
no code implementations • 2 Dec 2022 • Ming Jin, Vanshaj Khattar, Harshal Kaushik, Bilgehan Sel, Ruoxi Jia
We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension.
no code implementations • 19 Nov 2022 • Yuhao Ding, Ming Jin, Javad Lavaei
We study risk-sensitive reinforcement learning (RL) based on an entropic risk measure in episodic non-stationary Markov decision processes (MDPs).
no code implementations • 9 Nov 2022 • Dawei Gao, Qinghua Guo, Ming Jin, Guisheng Liao, Yonina C. Eldar
Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance.
no code implementations • 1 Nov 2022 • Enwei Zhu, Yiyang Liu, Ming Jin, Jinpeng Li
However, existing nested NER models heavily rely on training data annotated with nested entities, while labeling such data is costly.
no code implementations • 25 Oct 2022 • Zhaoji Zhang, Qinghua Guo, Ying Li, Ming Jin, Chongwen Huang
Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem.
1 code implementation • 12 Oct 2022 • Yi Zeng, Minzhou Pan, Himanshu Jahagirdar, Ming Jin, Lingjuan Lyu, Ruoxi Jia
Most poisoning defenses presume access to a set of clean data (or base set).
no code implementations • 23 Feb 2022 • Zain ul Abdeen, He Yin, Vassilis Kekatos, Ming Jin
In this paper, we examine an important problem of learning neural networks that certifiably meet certain specifications on input-output behaviors.
1 code implementation • 17 Feb 2022 • Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.
no code implementations • 11 Feb 2022 • Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.
no code implementations • 20 Nov 2021 • Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li
To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.
3 code implementations • ICLR 2022 • Yi Zeng, Si Chen, Won Park, Z. Morley Mao, Ming Jin, Ruoxi Jia
Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size.
no code implementations • 29 Sep 2021 • Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia
In this paper, we focus on the problem of identifying bad training data when the underlying cause is unknown in advance.
no code implementations • 29 Sep 2021 • Ming Jin, Yuan-Fang Li, Yu Zheng, Bin Yang, Shirui Pan
Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.
1 code implementation • 8 Sep 2021 • Fangda Gu, He Yin, Laurent El Ghaoui, Murat Arcak, Peter Seiler, Ming Jin
Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.
1 code implementation • 23 Aug 2021 • Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Yi-Ping Phoebe Chen
While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.
1 code implementation • 16 Jul 2021 • Hao Chen, Ming Jin, Zhunan Li, Cunhang Fan, Jinpeng Li, Huiguang He
Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation.
no code implementations • 10 Jun 2021 • Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia
High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM).
1 code implementation • 12 May 2021 • Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan
To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.
no code implementations • 2 May 2021 • Sarthak Gupta, Vassilis Kekatos, Ming Jin
The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions.
3 code implementations • 27 Feb 2021 • Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, Philip S. Yu
Deep learning on graphs has attracted significant interests recently.
no code implementations • 25 Jan 2021 • Man Luo, Qinghua Guo, Ming Jin, Yonina C. Eldar, Defeng, Huang, Xiangming Meng
Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm.
1 code implementation • 16 Dec 2020 • He Yin, Peter Seiler, Ming Jin, Murat Arcak
A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL).
no code implementations • 25 Sep 2019 • Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
1 code implementation • 24 May 2019 • Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
no code implementations • 26 Oct 2018 • Ming Jin, Javad Lavaei
We investigate the important problem of certifying stability of reinforcement learning policies when interconnected with nonlinear dynamical systems.
no code implementations • 26 Dec 2015 • Ming Jin, Andreas Damianou, Pieter Abbeel, Costas Spanos
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations.
no code implementations • 22 Jun 2014 • Ming Jin, Han Zou, Kevin Weekly, Ruoxi Jia, Alexandre M. Bayen, Costas J. Spanos
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors.